1. Nowcast Deep Learning Models For Constraining Zero-Day Pathogen Attacks – Application on Chest Radiographs to Covid-19
- Author
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Wan Hang Keith Chiu, Thomas Wing Yan Chin, Sailong Zhang, Christine Shing Yen Lo, Philip Lh Yu, Jonan Chun Yin Lee, R Du, Varut Vardhanabhuti, Alistair Yun Hee Yap, Siu Ting Leung, Ming-Yen Ng, Ambrose Ho Tung Fong, Benjamin Xin Hao Fang, Macy Mei Sze Lui, Michael D. Kuo, and Dymtro Poplavskiy
- Subjects
Coronavirus disease 2019 (COVID-19) ,business.industry ,Deep learning ,Radiography ,Zero (complex analysis) ,Medicine ,Artificial intelligence ,business ,Machine learning ,computer.software_genre ,computer - Abstract
Outbreaks due to emergent pathogens like Covid-19 are difficult to contain as the time to gather sufficient information to develop a detection system is outpaced by the speed of transmission. Here we develop a general pneumonia (PNA) CXR Deep Learning (DL) model (MAIL1.0) follow by a second-generation DL model (MAIL2.0) for detection of Covid-19 on chest radiographs (CXR). We validate the models on two prospective cohorts of high-risks patients screened for Covid-19 reverse transcriptase-polymerase chain reaction (RT-PCR). MAIL1.0 has an Area Under the Receiver Operating Characteristics (AUC) of 0.93, sensitivity and specificity of 90.5% and 76.7% in detection of visible pneumonia and MAIL2.0 has an AUC of 0.81, sensitivity and specificity of 84.7% and 71.6%, significantly outperforming radiologists, especially amongst asymptomatic and patients presenting with early symptoms. Nowcast DL models may be an effective tool in helping to constrain the outbreak, particularly in resource-stretched healthcare systems.
- Published
- 2020
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